# TODO: 导入必要的库和模块
import numpy as np
import matplotlib.pyplot as plt
from sklearn.datasets import load_digits
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier
import pickle
from tqdm import tqdm
# TODO: 加载数字数据集
digits = load_digits()
X = digits.data
y = digits.target
# TODO: 将数据集划分为训练集和测试集
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
# TODO: 初始化变量以存储最佳准确率，相应的k值和最佳knn模型
best_accuracy = 0
best_k = 0
best_knn = None
# TODO: 初始化一个列表以存储每个k值的准确率
accuracy_list = []
# TODO: 尝试从1到40的k值，对于每个k值，训练knn模型，保存最佳准确率，k值和knn模型
for k in tqdm(range(1, 41)):
    knn = KNeighborsClassifier(n_neighbors=k)
    knn.fit(X_train, y_train)
    accuracy = knn.score(X_test, y_test)
    accuracy_list.append(accuracy)
    if accuracy > best_accuracy:
        best_accuracy = accuracy
        best_k = k
        best_knn = knn
plt.figure(figsize=(10, 6))
plt.plot(range(1, 41), accuracy_list, marker='o')
plt.axvline(x=best_k, color='red', linestyle='--')
plt.text(best_k + 0.5, best_accuracy, f'k={best_k}, Accuracy={best_accuracy:.3f}', color='red')
plt.xlabel('k value')
plt.ylabel('Accuracy')
plt.title('Accuracy of different k values')
plt.savefig('accuracy_plot.pdf')
plt.close()
# TODO: 将最佳KNN模型保存到二进制文件
with open('best_knn_model.pkl', 'wb') as f:
    pickle.dump(best_knn, f)
# TODO: 打印最佳准确率和相应的k值
print(f'The best accuracy is {best_accuracy:.3f}, corresponding k value is {best_k}')